Equipment downtime is one of the chief reasons for excess production time in the manufacturing industry, triggering production delays and a drop in revenue in response. Downtime is atime when a machine or device is not in operation.
At any point in the production line, unpredicted equipment downtime will lead to lossof productivity and resources.It will seriously influence efficiency when a machine goes down.
This will result in delayed deliveries, increased maintenance costs, loss of consumer interest, weakened credibility, and higher labor costs.
Is that possible to predict machine failures and prevent downtime?
“Yes,” it is possible. Manufacturers now have the tools to introduce predictive maintenance on their machines with the introduction of IoT software, an alternative to unexpected downtime delays.
Since failure is unpredictable, corrective maintenance can be extremely time-consuming, while scheduled periodic maintenance can result in over-maintenance of resources, spending hours, money, and energy.
Organizations that manage, track, and evaluate resource health data would be able to predict better and efficiently schedule when maintenance is needed.
Industry 4.0 innovations to tackle downtime
Most of the products are interconnected in a smart factory, thanks to IoT sensors and IoT apps.Until the case, these related industrial resources will be able to notify you that a failure is about to develop.
Smart IoT sensors are used throughout the chain to provide this information. Once the data is gathered through sensors, intelligent IoT solutions can perform predictive analytics to proactively figure out the outcomes.
With real-time analytics, manufacturers can know when and how a resource is going to fail and can take preventive measures to avoid downtime.
How can IoT reduce production downtime?
IoT technology aims to reduce production downtime in the following ways:
- Proactive maintenance
Manufacturers can able to monitor the status of all machines in their factories. They can now shut out any potential issues before they get more complex enough to cause downtime.
- In-depth assessment
Not just the machines as a whole, trackingeach of the parts can also be done. Analytics can predict before certain parts will fail and replace them before they ever occur.
- Parameter-based real-time maintenance
Observe machine conditions such as status, loads, temperature,pressure, speed, usage, and other usefulparameters in real-time to determine if thresholds are exceeded.
When they breach the agreed thresholds IoT capabilities will trigger actions to ensure effective maintenance and increase their uptime.
- Analytics for preventive actions and planning
It also tracks precisely when, where, and how a downtime happens. To stop and troubleshoot downtime issues, speed and accuracy are crucial. Also, analyzing and diagnosing alerts can be optimized.
Understanding the common issues of machines that need frequent consideration by evaluating their frequency can alert machines that can cause issues.
Conquering equipment downtime complications with IoT solutions
IoT-driven technologies have brought manufacturing segments closer to limiting unplanned downtime.
IoT-enabled resource management and predictive maintenance strategies can help in impressive reductions in unpredicted downtime, advancing the company’s manufacturing capabilities and efficiencies.
Sterison offers the best IoT solutions for production efficiency monitoring to view the metrics of the machines in your factory.
